With rapid advances in digital storage devices, networks, and data compression techniques, the volume of digital images throughout the world has been ever growing and rapidly expanding. Additionally, due to the widespread household use of personal computers and the World Wide Web (Web), digital images are regularly uploaded, downloaded, shared, and searched for among Web users. Thus, it is no surprise that the Web has become an enormous repository of data including, in particular, digital images. To effectively manage this voluminous amount of digital images, a promising approach is to annotate them with keywords.
Concept labeling and ontology-free tagging (tagging) are the two typical manners of image annotation. Concept labeling involves presenting an image and a corresponding set of fixed keywords to a user and allowing the user to determine each keyword's relevance. On the other hand, tagging involves allowing users to create image tags independent of any domain-specific rules. However, despite extensive research, efforts have been mainly dedicated towards advancing concept labeling. Unfortunately, automatic image labeling algorithms are still far from satisfactory and manual labeling is rather labor-intensive. In contrast, however, tagging offers more freedom to users and provides a better experience during manual annotation.
Unfortunately, adequate tools do not exist for effectively and/or efficiently tagging digital images on the Web. Existing tagging tools merely provide manual tagging scenarios.